Occupant classification systems are commonly used in motor vehicles for determining if pyrotechnically deployed restraints such as air bags should be deployed in the event of a sufficiently severe crash. Early systems relied exclusively on sensors for measuring physical parameters such as seat force, but vision-based systems have become economically attractive due to the advent of low-cost solid-state imaging chips. However, image processing algorithms for accurately detecting and classifying vehicle occupants can be relatively complex, resulting in slower than desired response to changes in occupant position. The algorithms can be simplified to provide faster dynamic response time, but this typically impairs the classification accuracy. Accordingly, what is needed is a classification method having both high classification accuracy and fast dynamic response.